diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R
index a99740f64..01de306a0 100644
--- a/R-package/R/xgb.train.R
+++ b/R-package/R/xgb.train.R
@@ -66,7 +66,11 @@
 #'   prediction and dtrain,
 #' @param verbose If 0, xgboost will stay silent. If 1, xgboost will print 
 #'   information of performance. If 2, xgboost will print information of both
-#'
+#' @param earlyStopRound If \code{NULL}, the early stopping function is not triggered. 
+#'     If set to an integer \code{k}, training with a validation set will stop if the performance 
+#'     keeps getting worse consecutively for \code{k} rounds.
+#' @param maximize If \code{feval} and \code{earlyStopRound} are set, then \code{maximize} must be set as well.
+#'     \code{maximize=TRUE} means the larger the evaluation score the better.
 #' @param ... other parameters to pass to \code{params}.
 #' 
 #' @details 
@@ -114,7 +118,8 @@
 #' @export
 #' 
 xgb.train <- function(params=list(), data, nrounds, watchlist = list(), 
-                      obj = NULL, feval = NULL, verbose = 1, ...) {
+                      obj = NULL, feval = NULL, verbose = 1, 
+                      earlyStopRound = NULL, maximize = NULL, ...) {
   dtrain <- data
   if (typeof(params) != "list") {
     stop("xgb.train: first argument params must be list")
@@ -133,6 +138,36 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
   }
   params = append(params, list(...))
   
+  # Early stopping
+  if (!is.null(earlyStopRound)){
+    if (!is.null(feval) && is.null(maximize))
+      stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
+    if (length(watchlist) == 0)
+      stop('For early stopping you need at least one set in watchlist.')
+    if (is.null(maximize) && is.null(params$eval_metric))
+      stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
+    if (is.null(maximize))
+    {
+      if (params$eval_metric %in% c('rmse','logloss','error','merror','mlogloss')) {
+        maximize = FALSE
+      } else {
+        maximize = TRUE
+      }
+    }
+    
+    if (maximize) {
+      bestScore = 0
+    } else {
+      bestScore = Inf
+    }
+    bestInd = 0
+    earlyStopflag = FALSE
+    
+    if (length(watchlist)>1)
+      warning('Only the first data set in watchlist is used for early stopping process.')
+  }
+  
+  
   handle <- xgb.Booster(params, append(watchlist, dtrain))
   bst <- xgb.handleToBooster(handle)
   for (i in 1:nrounds) {
@@ -140,8 +175,27 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
     if (length(watchlist) != 0) {
       msg <- xgb.iter.eval(bst$handle, watchlist, i - 1, feval)
       cat(paste(msg, "\n", sep=""))
+      if (!is.null(earlyStopRound))
+      {
+        score = strsplit(msg,':|\\s+')[[1]][3]
+        score = as.numeric(score)
+        if ((maximize && score>bestScore) || (!maximize && score<bestScore)) {
+          bestScore = score
+          bestInd = i
+        } else {
+          if (i-bestInd>earlyStopRound) {
+            earlyStopflag = TRUE
+            cat('Stopping. Best iteration:',bestInd)
+            break
+          }
+        }
+      }
     }
   }
   bst <- xgb.Booster.check(bst)
+  if (!is.null(earlyStopRound)) {
+    bst$bestScore = bestScore
+    bst$bestInd = bestInd
+  }
   return(bst)
 } 
diff --git a/R-package/R/xgboost.R b/R-package/R/xgboost.R
index ede53b116..367a149e7 100644
--- a/R-package/R/xgboost.R
+++ b/R-package/R/xgboost.R
@@ -30,6 +30,11 @@
 #'   performance and construction progress information
 #' @param missing Missing is only used when input is dense matrix, pick a float 
 #'     value that represents missing value. Sometimes a data use 0 or other extreme value to represents missing values.
+#' @param earlyStopRound If \code{NULL}, the early stopping function is not triggered. 
+#'     If set to an integer \code{k}, training with a validation set will stop if the performance 
+#'     keeps getting worse consecutively for \code{k} rounds.
+#' @param maximize If \code{feval} and \code{earlyStopRound} are set, then \code{maximize} must be set as well.
+#'     \code{maximize=TRUE} means the larger the evaluation score the better.
 #' @param ... other parameters to pass to \code{params}.
 #' 
 #' @details 
@@ -51,7 +56,7 @@
 #' @export
 #' 
 xgboost <- function(data = NULL, label = NULL, missing = NULL, params = list(), nrounds, 
-                    verbose = 1, ...) {
+                    verbose = 1, earlyStopRound = NULL, maximize = NULL, ...) {
   if (is.null(missing)) {
     dtrain <- xgb.get.DMatrix(data, label)
   } else {
@@ -66,7 +71,8 @@ xgboost <- function(data = NULL, label = NULL, missing = NULL, params = list(),
     watchlist <- list()
   }
   
-  bst <- xgb.train(params, dtrain, nrounds, watchlist, verbose=verbose)
+  bst <- xgb.train(params, dtrain, nrounds, watchlist, verbose = verbose,
+                   earlyStopRound = earlyStopRound)
   
   return(bst)
 } 
diff --git a/R-package/demo/00Index b/R-package/demo/00Index
index 969da0d91..f0b41ec2a 100644
--- a/R-package/demo/00Index
+++ b/R-package/demo/00Index
@@ -6,3 +6,4 @@ generalized_linear_model        Generalized Linear Model
 cross_validation                Cross validation
 create_sparse_matrix            Create Sparse Matrix
 predict_leaf_indices            Predicting the corresponding leaves
+early_Stopping                  Early Stop in training
diff --git a/R-package/demo/early_Stopping.R b/R-package/demo/early_Stopping.R
new file mode 100644
index 000000000..4cab385ca
--- /dev/null
+++ b/R-package/demo/early_Stopping.R
@@ -0,0 +1,37 @@
+require(xgboost)
+# load in the agaricus dataset
+data(agaricus.train, package='xgboost')
+data(agaricus.test, package='xgboost')
+dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
+dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
+# note: for customized objective function, we leave objective as default
+# note: what we are getting is margin value in prediction
+# you must know what you are doing
+param <- list(max.depth=2,eta=1,nthread = 2, silent=1)
+watchlist <- list(eval = dtest)
+num_round <- 20
+# user define objective function, given prediction, return gradient and second order gradient
+# this is loglikelihood loss
+logregobj <- function(preds, dtrain) {
+  labels <- getinfo(dtrain, "label")
+  preds <- 1/(1 + exp(-preds))
+  grad <- preds - labels
+  hess <- preds * (1 - preds)
+  return(list(grad = grad, hess = hess))
+}
+# user defined evaluation function, return a pair metric_name, result
+# NOTE: when you do customized loss function, the default prediction value is margin
+# this may make buildin evalution metric not function properly
+# for example, we are doing logistic loss, the prediction is score before logistic transformation
+# the buildin evaluation error assumes input is after logistic transformation
+# Take this in mind when you use the customization, and maybe you need write customized evaluation function
+evalerror <- function(preds, dtrain) {
+  labels <- getinfo(dtrain, "label")
+  err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
+  return(list(metric = "error", value = err))
+}
+print ('start training with early Stopping setting')
+# training with customized objective, we can also do step by step training
+# simply look at xgboost.py's implementation of train
+bst <- xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror, maximize = FALSE,
+                 earlyStopRound = 3)
diff --git a/R-package/man/xgb.train.Rd b/R-package/man/xgb.train.Rd
index 1bd243d60..4444b95ae 100644
--- a/R-package/man/xgb.train.Rd
+++ b/R-package/man/xgb.train.Rd
@@ -5,7 +5,8 @@
 \title{eXtreme Gradient Boosting Training}
 \usage{
 xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
-  feval = NULL, verbose = 1, ...)
+  feval = NULL, verbose = 1, earlyStopRound = NULL, maximize = NULL,
+  ...)
 }
 \arguments{
 \item{params}{the list of parameters.
@@ -49,7 +50,7 @@ xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
     \item \code{binary:logistic} logistic regression for binary classification. Output probability.
     \item \code{binary:logitraw} logistic regression for binary classification, output score before logistic transformation.
     \item \code{num_class} set the number of classes. To use only with multiclass objectives.
-    \item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective. Class is a number and should be from 0 \code{tonum_class}
+    \item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective. Class is represented by a number and should be from 0 to \code{tonum_class}.
     \item \code{multi:softprob} same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class.
     \item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
   }
@@ -75,7 +76,14 @@ gradient with given prediction and dtrain,}
 prediction and dtrain,}
 
 \item{verbose}{If 0, xgboost will stay silent. If 1, xgboost will print
-  information of performance. If 2, xgboost will print information of both}
+information of performance. If 2, xgboost will print information of both}
+
+\item{earlyStopRound}{If \code{NULL}, the early stopping function is not triggered.
+If set to an integer \code{k}, training with a validation set will stop if the performance
+keeps getting worse consecutively for \code{k} rounds.}
+
+\item{maximize}{If \code{feval} and \code{earlyStopRound} are set, then \code{maximize} must be set as well.
+\code{maximize=TRUE} means the larger the evaluation score the better.}
 
 \item{...}{other parameters to pass to \code{params}.}
 }
@@ -98,7 +106,7 @@ Number of threads can also be manually specified via \code{nthread} parameter.
      \item \code{error} Binary classification error rate. It is calculated as \code{(wrong cases) / (all cases)}. For the predictions, the evaluation will regard the instances with prediction value larger than 0.5 as positive instances, and the others as negative instances.
      \item \code{merror} Multiclass classification error rate. It is calculated as \code{(wrong cases) / (all cases)}.
      \item \code{auc} Area under the curve. \url{http://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
-     \item \code{ndcg} Normalized Discounted Cumulative Gain. \url{http://en.wikipedia.org/wiki/NDCG}
+     \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{http://en.wikipedia.org/wiki/NDCG}
   }
 
 Full list of parameters is available in the Wiki \url{https://github.com/dmlc/xgboost/wiki/Parameters}.
diff --git a/R-package/man/xgboost.Rd b/R-package/man/xgboost.Rd
index 79cff207a..9509dbd39 100644
--- a/R-package/man/xgboost.Rd
+++ b/R-package/man/xgboost.Rd
@@ -5,7 +5,7 @@
 \title{eXtreme Gradient Boosting (Tree) library}
 \usage{
 xgboost(data = NULL, label = NULL, missing = NULL, params = list(),
-  nrounds, verbose = 1, ...)
+  nrounds, verbose = 1, earlyStopRound = NULL, maximize = NULL, ...)
 }
 \arguments{
 \item{data}{takes \code{matrix}, \code{dgCMatrix}, local data file or
@@ -41,6 +41,13 @@ Commonly used ones are:
 information of performance. If 2, xgboost will print information of both
 performance and construction progress information}
 
+\item{earlyStopRound}{If \code{NULL}, the early stopping function is not triggered.
+If set to an integer \code{k}, training with a validation set will stop if the performance
+keeps getting worse consecutively for \code{k} rounds.}
+
+\item{maximize}{If \code{feval} and \code{earlyStopRound} are set, then \code{maximize} must be set as well.
+\code{maximize=TRUE} means the larger the evaluation score the better.}
+
 \item{...}{other parameters to pass to \code{params}.}
 }
 \description{